Predicting cardiovascular events using three stage Discriminant Function is much more accurate than Framingham or QRISK

The two best known approaches to predicting cardiovascular risk are Framingham and QRISK. Both methods correctly predict less than 70% of cases, with a high ratio of false positive predictions to true predictions. Each uses a combination of predictors that is applied to the data only once. The present approach uses the Discriminant Function with multiple applications. A British sample of data on cardiovascular risk was analysed. Principal Components analysis was used to reveal the underlying structure of the data—it identified four independent determinants of the data. Discriminant Function analysis in three stages was then used to accommodate the difficulties of dealing with multiple determinants. Ninety-four percent of the cases with cardiovascular incidents (CVI) were predicted correctly up to more than 20 years ahead, with a misclassification rate overall of 2.8 errors for every one correct. When checked for likely shrinkage from sample to sample using the Jacknife method 92% of CVI’s were correctly predicted. Instead of a single application of a linear combination of predictors to find those people most likely to have cardiovascular events a repeated application of the predictors to the residuals from the previous prediction stage is likely to find a much higher proportion of true predictions and with much less error. The results also allow for a simple way of conveying the risk of CVI to individual patients.